秘鲁南部豚鼠(Cavia Porcellus)的形态评估

Q4 Multidisciplinary
Dennis Quispe Condori, Ferdynand Marcos Huacani Pacori, Javier Mamani Paredes, R. Mamani-Cato
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引用次数: 0

摘要

这项研究的目的是对秘鲁南部的豚鼠进行形态测量评估。研究在海拔 3824 米的普诺国家农业创新研究所(NIAI)下属的伊尔帕农业试验站(AESI)进行。共使用了 120 只豚鼠(雌性 60 只,雄性 60 只),平均年龄为 22 天。评估的形态特征包括:体重(BW)、胸围(CC)、腹围(AP)、颈围(NP)、头长(HL)和头宽(HW)。为了评估性别对形态特征的影响,采用了完全随机设计。为了确定最能预测体重的方程,使用了逐步回归法,并使用皮尔逊相关法获得了形态特征之间的相关性。结果表明,性别因素对体重、CC、AP、NP、HL 和 HW 没有显著影响(P≥0.05);同样,预测豚鼠体重的最佳方程为BW=-530.50+21.98(CC)+12.72(AP)+10.16(NP)+57.23(HW),R2=84%。形态特征之间的皮尔逊相关性很大,呈正相关,且具有统计学意义(P<0.001)。结论是,在秘鲁高原条件下,性别因素不会影响形态特征。根据 CC、AP、NP 和 HW 预测体重也是可行的,而且相关性很高,呈正相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Morphometric evaluation of guinea pigs (Cavia Porcellus) in Southern Peru
The aim of this study was the morphometric evaluation of guinea pigs in southern Peru. The study was carried out at the Agrarian Experimental Station Illpa (AESI) of the National Institute of Agrarian Innovation (NIAI) in Puno at 3824 meters above sea level. 120 guinea pigs were used (females n = 60 and males n = 60) with an average age of 22 days. The morphometric characteristics evaluated were: body weight (BW), chest circumference (CC), abdominal perimeter (AP), neck perimeter (NP), head length (HL) and head width (HW). To evaluate the effect of sex on morphometric characteristics, a completely randomized design was used. To determine the equation that best predicts body weight, stepwise regression was used, and correlations between morphometric characteristics were obtained using Pearson's correlation. The results show that the sex factor does not significantly influence BW, CC, AP, NP, HL, and HW (p≥0.05); likewise, the equation that best predicts the body weight of the guinea pigs was: BW = -530.50 + 21.98(CC) + 12.72(AP) + 10.16(NP) + 57.23(HW), with R2 = 84%. Pearson correlations between morphometric characteristics were of high magnitude, positive, and statistically significant (p<0.001). It is concluded that in conditions of the Peruvian highlands, the sex factor does not influence the morphometric characteristics. It is also possible to predict body weight from CC, AP, NP, and HW, and the correlations were high and positive.
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来源期刊
Edelweiss Applied Science and Technology
Edelweiss Applied Science and Technology Multidisciplinary-Multidisciplinary
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